Modeling Consumer Decision Making and Discrete Choice Behavior

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EconometricMethodology The Sample and MeasurementPopulation MeasuremeCharacteristics.
InferencePopulation MeasurementEconometricCharacteristicsBehavior Patterns.
Classical InferencePopulation MeasurementEconometricCharacteristicsImprecise inference Behavior Patterns.
about the entirepopulation sampling Choicestheory and asymptotics Bayesian InferencePopulation Econometric Measurement.
CharacteristicsSharp exact inference Behaviorabout only the sample the posterior to the data Patterns Econometric Frameworks.
Nonparametric Semiparametric Parametric Classical Sampling Theory Bayesian.
We will focus mainly onclassical parametric ethods Objectives in Model Building Specification guided by underlying theory Modeling framework.
Functional forms Estimation coefficients partial effects model implications Statistical inference hypothesis testing Prediction individual and aggregate Model assessment fit adequacy and evaluation.
Model extensions Interdependencies multiple part models Heterogeneity Endogeneity Exploration Estimation and inference methods.
Regression BasicsThe MODEL Modeling the conditional mean RegressionOther features of interest Modeling quantiles.
Conditional variances or covariances Modeling probabilities for discrete choice Modeling other features of the population Application Health Care UsageGerman Health Care Usage Data 7 293 Individuals Varying Numbers of Periods.
Data downloaded from Journal of Applied Econometrics Archive This is an unbalanced panel with 7 293individuals They can be used for regression count models binary choice ordered choice and bivariate binarychoice This is a large data set There are altogether 27 326 observations The number of observations rangesfrom 1 to 7 Frequencies are 1 1525 2 2158 3 825 4 926 5 1051 6 1000 7 987 Downloaded from theJAE Archive .
Variables in the file areDOCTOR 1 Number of doctor visits 0 HOSPITAL 1 Number of hospital visits 0 HSAT health satisfaction coded 0 low 10 high DOCVIS number of doctor visits in last three months.
HOSPVIS number of hospital visits in last calendar yearPUBLIC insured in public health insurance 1 otherwise 0ADDON insured by add on insurance 1 otherswise 0HHNINC household nominal monthly net income in German marks 10000 4 observations with income 0 were dropped .
HHKIDS children under age 16 in the household 1 otherwise 0EDUC years of schoolingAGE age in yearsMARRIED marital status Household Income.
Kernel Density Estimator Histogram Regression Income on Education Ordinary least squares regression LHS LOGINC Mean 92882.
Standard deviation 47948Number of observs 887Model size Parameters 2Degrees of freedom 885Residuals Sum of squares 183 19359.
Standard error of e 45497Fit R squared 10064Adjusted R squared 09962Model test F 1 885 prob 99 0 0000 Diagnostic Log likelihood 559 06527.
Restricted b 0 606 10609Chi sq 1 prob 94 1 0000 Info criter LogAmemiya Prd Crt 1 57279 Variable Coefficient Standard Error b St Er P Z z Mean of X.
Constant 1 71604 08057 21 299 0000EDUC 07176 00721 9 951 0000 10 9707 Note Significance at 1 5 10 level .
Specification and Functional Form Ordinary least squares regression LHS LOGINC Mean 92882.
Standard deviation 47948Number of observs 887Model size Parameters 3Degrees of freedom 884Residuals Sum of squares 183 00347.
Standard error of e 45499Fit R squared 10157Adjusted R squared 09954Model test F 2 884 prob 50 0 0000 Diagnostic Log likelihood 558 60477.
Restricted b 0 606 10609Chi sq 2 prob 95 0 0000 Info criter LogAmemiya Prd Crt 1 57158 Variable Coefficient Standard Error b St Er P Z z Mean of X.
Constant 1 68303 08763 19 207 0000EDUC 06993 00746 9 375 0000 10 9707FEMALE 03065 03199 958 3379 42277 .
Interesting Partial Effects Ordinary least squares regression LHS LOGINC Mean 92882Standard deviation 47948.
Number of observs 887Model size Parameters 5Degrees of freedom 882Residuals Sum of squares 171 87964Standard error of e 44145.
Fit R squared 15618Adjusted R squared 15235 E Income x Model test F 4 882 prob 40 8 0000 Age 2 Age Age2Diagnostic Log likelihood.
Restricted b 0 530 79258 606 10609Chi sq 4 prob 150 6 0000 Info criter LogAmemiya Prd Crt 1 62978.
Variable Coefficient Standard Error b St Er P Z z Mean of X Constant 5 26676 56499 9 322 0000EDUC 06469 00730 8 860 0000 10 9707.
FEMALE 03683 03134 1 175 2399 42277AGE 15567 02297 6 777 0000 50 4780AGE2 00161 00023 7 014 0000 2620 79 Impact of Age on Income.
Inference Does the same model apply to men and women Subsample analyzed for this command is FEMALE 0Residuals Sum of squares 90 80587Number of observs 512Fit R squared 16538.
Variable Coefficient Standard Error b St Er P Z z Mean of X Constant 5 45890 67675 8 066 0000EDUC 06626 00836 7 924 0000 11 4342.
AGE 15894 02789 5 699 0000 49 3750AGE2 00160 00028 5 695 0000 2514 12 Subsample analyzed for this command is FEMALE 1Residuals Sum of squares 79 45629.
Number of observs 375Fit R squared 14008 Constant 4 10991 1 03733 3 962 0001EDUC 05850 01399 4 180 0000 10 3378.
AGE 11707 04103 2 853 0046 51 9840AGE2 00129 00040 3 210 0014 2766 43 Residuals Sum of squares 172 14877Number of observs 887.
Fit R squared 15486 Constant 5 24401 56478 9 285 0000EDUC 06676 00709 9 418 0000 10 9707AGE 15342 02290 6 701 0000 50 4780.
AGE2 00159 00023 6 944 0000 2620 79 F 172 14877 90 80587 79 45629 ... 90 80587 79 45629 512 375 2 4... 2 43 F 2 38Classical Inference. Population. Measurement. Characteristics. Behavior Patterns. Choices. Imprecise inference about the entire population – sampling theory and asymptotics

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